Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "43" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460013 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.540375 | 0.227571 | -0.446595 | 0.746225 | -0.909462 | 1.004776 | -1.290697 | 1.024028 | 0.6029 | 0.6106 | 0.3532 | nan | nan |
| 2460012 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.328409 | 0.105816 | -0.571282 | 0.575558 | -0.901420 | 1.203360 | -0.714373 | 1.874488 | 0.5978 | 0.6060 | 0.3502 | nan | nan |
| 2460011 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.410225 | -0.037455 | -1.293675 | 0.556651 | -0.805273 | 2.105800 | -0.900255 | 0.607231 | 0.5931 | 0.5973 | 0.3684 | nan | nan |
| 2460010 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.754919 | 0.165684 | -0.613353 | 0.861747 | -1.251985 | 0.971065 | -0.678889 | 1.014883 | 0.5956 | 0.6002 | 0.3765 | nan | nan |
| 2460009 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.204217 | -0.010822 | -1.039673 | 0.946128 | -0.765041 | 1.294546 | -1.202815 | 1.129650 | 0.6023 | 0.6072 | 0.3804 | nan | nan |
| 2460008 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.309607 | 0.148658 | -0.443525 | 1.002985 | 1.679569 | 1.026468 | -0.369919 | 1.040439 | 0.6452 | 0.6544 | 0.3401 | nan | nan |
| 2460007 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.413695 | 0.114160 | -0.155570 | 0.964959 | -0.863204 | 0.984655 | -1.511152 | 0.789754 | 0.6134 | 0.6165 | 0.3624 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 89.14% | 86.05% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1625 | 0.1791 | 0.0890 | nan | nan |
| 2459998 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.070207 | 0.086527 | -0.152509 | 0.768716 | -0.904215 | 1.217834 | -1.282096 | 0.380433 | 0.6204 | 0.6268 | 0.3746 | nan | nan |
| 2459997 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.066642 | 0.196612 | 0.042915 | 0.897039 | -0.828472 | 0.677375 | -1.069371 | 1.332580 | 0.6311 | 0.6440 | 0.3785 | nan | nan |
| 2459996 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.055578 | 0.270031 | -0.252445 | 1.100521 | -0.884481 | 0.941359 | -0.365463 | 1.110227 | 0.6382 | 0.6498 | 0.3885 | nan | nan |
| 2459995 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.258426 | 0.031240 | -0.142303 | 0.738830 | -0.544701 | 1.170991 | -1.051826 | 0.566991 | 0.6309 | 0.6443 | 0.3812 | nan | nan |
| 2459994 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.098762 | -0.064966 | -0.002346 | 0.903454 | -0.803367 | 0.665386 | -0.978397 | 1.240718 | 0.6254 | 0.6354 | 0.3812 | nan | nan |
| 2459993 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.255603 | 0.022320 | 0.237694 | 0.723123 | -0.479392 | 0.120835 | -1.099382 | 0.963144 | 0.6212 | 0.6404 | 0.3935 | nan | nan |
| 2459991 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.366040 | -0.003412 | 0.178665 | 0.798092 | -0.727943 | 0.717802 | -0.932082 | 0.731080 | 0.6329 | 0.6355 | 0.3814 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.021949 | 0.059466 | 0.192011 | 0.744157 | -0.815310 | 0.579821 | -0.669682 | 0.897450 | 0.6358 | 0.6398 | 0.3805 | nan | nan |
| 2459989 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.223389 | 0.215068 | 0.371593 | 0.911261 | -0.701207 | 0.632409 | -0.571468 | 0.743405 | 0.6308 | 0.6383 | 0.3830 | nan | nan |
| 2459988 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.301002 | -0.004294 | 0.133061 | 0.641935 | -1.014204 | 0.318901 | -0.781312 | 0.696351 | 0.6210 | 0.6277 | 0.3763 | nan | nan |
| 2459987 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.160480 | -0.063627 | -0.253604 | 0.780305 | 3.387341 | 0.797126 | 0.723869 | 1.550078 | 0.6346 | 0.6416 | 0.3707 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.042448 | -0.028540 | -0.021029 | 0.712224 | -0.634497 | 0.539112 | 0.047200 | 1.641979 | 0.6402 | 0.6535 | 0.3297 | nan | nan |
| 2459985 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.175134 | -0.008544 | -0.238278 | 0.738525 | 0.160886 | 0.472900 | -0.747995 | 1.744897 | 0.6211 | 0.6274 | 0.3744 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.099135 | 0.255954 | -0.285288 | 0.713755 | 2.973647 | 0.635964 | 0.063410 | 0.995880 | 0.6447 | 0.6511 | 0.3554 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.082577 | 0.448696 | 0.032732 | 0.649589 | -0.730404 | 0.358664 | 0.163299 | 2.044568 | 0.6531 | 0.6686 | 0.3144 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.192637 | 0.788380 | -0.443178 | 0.842160 | -0.515498 | 0.483603 | -1.177270 | 0.609097 | 0.7074 | 0.7062 | 0.2830 | nan | nan |
| 2459981 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.077265 | 0.675632 | 0.044718 | 0.601522 | -1.016822 | 0.053860 | -1.226693 | 0.590023 | 0.6341 | 0.6348 | 0.3719 | nan | nan |
| 2459980 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.100066 | 0.374715 | -0.266445 | 0.613482 | -1.051621 | 0.170250 | -0.883534 | 0.737358 | 0.6799 | 0.6850 | 0.3017 | nan | nan |
| 2459979 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.216500 | 0.602984 | -0.116439 | 0.586733 | -0.783337 | 0.251184 | -0.076848 | 1.128584 | 0.6242 | 0.6284 | 0.3716 | nan | nan |
| 2459978 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.122406 | 0.774683 | -0.003876 | 0.589283 | -0.979871 | 0.087939 | -1.045055 | 0.777289 | 0.6229 | 0.6259 | 0.3765 | nan | nan |
| 2459977 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.142208 | 0.599842 | -0.152976 | 0.596275 | -0.944585 | 0.402929 | -1.019687 | 1.124871 | 0.5934 | 0.5989 | 0.3425 | nan | nan |
| 2459976 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.137147 | 0.466846 | -0.165929 | 0.590779 | -0.786575 | 0.568419 | -0.470426 | 1.060730 | 0.6368 | 0.6390 | 0.3690 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.024028 | -0.540375 | 0.227571 | -0.446595 | 0.746225 | -0.909462 | 1.004776 | -1.290697 | 1.024028 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.874488 | -0.328409 | 0.105816 | -0.571282 | 0.575558 | -0.901420 | 1.203360 | -0.714373 | 1.874488 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Variability | 2.105800 | -0.410225 | -0.037455 | -1.293675 | 0.556651 | -0.805273 | 2.105800 | -0.900255 | 0.607231 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.014883 | -0.754919 | 0.165684 | -0.613353 | 0.861747 | -1.251985 | 0.971065 | -0.678889 | 1.014883 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Variability | 1.294546 | -0.204217 | -0.010822 | -1.039673 | 0.946128 | -0.765041 | 1.294546 | -1.202815 | 1.129650 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | ee Temporal Variability | 1.679569 | 0.148658 | -0.309607 | 1.002985 | -0.443525 | 1.026468 | 1.679569 | 1.040439 | -0.369919 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Variability | 0.984655 | -0.413695 | 0.114160 | -0.155570 | 0.964959 | -0.863204 | 0.984655 | -1.511152 | 0.789754 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Variability | 1.217834 | -0.070207 | 0.086527 | -0.152509 | 0.768716 | -0.904215 | 1.217834 | -1.282096 | 0.380433 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.332580 | -0.066642 | 0.196612 | 0.042915 | 0.897039 | -0.828472 | 0.677375 | -1.069371 | 1.332580 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.110227 | 0.055578 | 0.270031 | -0.252445 | 1.100521 | -0.884481 | 0.941359 | -0.365463 | 1.110227 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Variability | 1.170991 | -0.258426 | 0.031240 | -0.142303 | 0.738830 | -0.544701 | 1.170991 | -1.051826 | 0.566991 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.240718 | -0.098762 | -0.064966 | -0.002346 | 0.903454 | -0.803367 | 0.665386 | -0.978397 | 1.240718 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 0.963144 | -0.255603 | 0.022320 | 0.237694 | 0.723123 | -0.479392 | 0.120835 | -1.099382 | 0.963144 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Power | 0.798092 | -0.366040 | -0.003412 | 0.178665 | 0.798092 | -0.727943 | 0.717802 | -0.932082 | 0.731080 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 0.897450 | 0.059466 | -0.021949 | 0.744157 | 0.192011 | 0.579821 | -0.815310 | 0.897450 | -0.669682 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Power | 0.911261 | 0.215068 | 0.223389 | 0.911261 | 0.371593 | 0.632409 | -0.701207 | 0.743405 | -0.571468 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 0.696351 | -0.004294 | 0.301002 | 0.641935 | 0.133061 | 0.318901 | -1.014204 | 0.696351 | -0.781312 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | ee Temporal Variability | 3.387341 | 0.160480 | -0.063627 | -0.253604 | 0.780305 | 3.387341 | 0.797126 | 0.723869 | 1.550078 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.641979 | -0.028540 | 0.042448 | 0.712224 | -0.021029 | 0.539112 | -0.634497 | 1.641979 | 0.047200 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.744897 | -0.008544 | 0.175134 | 0.738525 | -0.238278 | 0.472900 | 0.160886 | 1.744897 | -0.747995 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | ee Temporal Variability | 2.973647 | 0.099135 | 0.255954 | -0.285288 | 0.713755 | 2.973647 | 0.635964 | 0.063410 | 0.995880 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 2.044568 | -0.082577 | 0.448696 | 0.032732 | 0.649589 | -0.730404 | 0.358664 | 0.163299 | 2.044568 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Power | 0.842160 | 0.192637 | 0.788380 | -0.443178 | 0.842160 | -0.515498 | 0.483603 | -1.177270 | 0.609097 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Shape | 0.675632 | 0.675632 | -0.077265 | 0.601522 | 0.044718 | 0.053860 | -1.016822 | 0.590023 | -1.226693 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 0.737358 | 0.374715 | -0.100066 | 0.613482 | -0.266445 | 0.170250 | -1.051621 | 0.737358 | -0.883534 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.128584 | -0.216500 | 0.602984 | -0.116439 | 0.586733 | -0.783337 | 0.251184 | -0.076848 | 1.128584 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 0.777289 | 0.774683 | -0.122406 | 0.589283 | -0.003876 | 0.087939 | -0.979871 | 0.777289 | -1.045055 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.124871 | -0.142208 | 0.599842 | -0.152976 | 0.596275 | -0.944585 | 0.402929 | -1.019687 | 1.124871 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43 | N05 | RF_maintenance | nn Temporal Discontinuties | 1.060730 | 0.466846 | -0.137147 | 0.590779 | -0.165929 | 0.568419 | -0.786575 | 1.060730 | -0.470426 |